Wasserstein distance

`stress_wass()`

:- A wrapper for the stress functions using the 2-Wasserstein distance

`stress_RM_w()`

:- a stressed model component (random variable) fulfills a constraint on its risk measure defined by a gamma function.

`stress_RM_mean_sd_w()`

:- a stressed model component (random variable) fulfills a constraint on its mean, standard deviation, and risk measure defined by a gamma function.

`stress_HARA_RM_w()`

:- a stressed model component (random variable) fulfills a constraint on its HARA utility defined by a, b and eta parameter and risk measure defined by a gamma function.

`stress_mean_sd_w()`

:- a stressed model component (random variable) fulfills a constraint on its mean and standard deviation.

`stress_mean_w()`

:- a stressed model component (random variable) fulfills a constraint on its mean.

Functions

`mean_stressed()`

:- sample mean of chosen stressed model components, subject to the calculated scenario weights.

`sd_stressed()`

:- sample standard deviation of chosen stressed model components, subject to the calculated scenario weights.

`var_stressed()`

:- sample variance of chosen stressed model components, subject to the calculated scenario weights.

`cor_stressed()`

:- sample correlation coefficient of chosen stressed model components, subject to the calculated scenario weights.

`cdf_stressed()`

:- the empirical distribution function of a stressed model component (random variable) under the scenario weights.

`rename_SWIM()`

:- Get a new SWIM object with desired names.

Features

`stress()`

:- A parameter “names” to all stress functions, which allows to name a stress differently than just “stress 1”, “stress 2”, etc.
- A parameter “log” that allows users to inspect weights’ statistics, including minimum, maximum, standard deviation, Gini coefficient, and entropy.

`sensitivity()`

:- A parameter “p” can be specified for the degree of Wasserstein distance.

- fix minor bug in
`summary()`

. - add
`base`

argument for`quantile_stressed()`

and an error message if the input has`wCol`

has dimension larger than 1.

`plot_quantile()`

:- the function plots the empirical quantile of model components, subject to scenario weights.

`plot_weights()`

:- the function plots the scenario weights of a stressed model against model components.

`stress_moment()`

:- add parameter “normalise” that allows to linearly normalise the
values called by
`nleqslv`

. - the function prints a table with the required and achieved moments and the absolute and relative error.

- add parameter “normalise” that allows to linearly normalise the
values called by
`stress_VaR_ES()`

:- add parameter “normalise” that allows to linearly normalise the
values before
`uniroot`

is applied.

- add parameter “normalise” that allows to linearly normalise the
values before

- fix bug in merging different stress objects.

- add vignette
- fix bug in
`merge()`

. - fix bug in
`sensitivity()`

.

`VaR_stressed()`

:- the function calculates the VaR of model components, subject to scenario weights.

`ES_stressed()`

:- the function calculates the ES of model components, subject to scenario weights.

`credit_data`

:- a data set containing aggregate losses from a credit portfolio, generated through a binomial credit model.

`stress_VaR()`

:- amendment to the calculation of scenario weights when the specified VaR cannot be achieved.
- returns a message if the achieved VaR is not equal to the stressed VaR specified.
- specs of the
`SWIM`

object contains the achieved VaR - allowing for stressing VaR downwards

`stress_VaR_ES()`

:- amendment analogous to the
`stress_VaR()`

. - returns a message if the achieved VaR is not equal to the stressed VaR specified.
- specs of the
`SWIM`

object contains the achieved VaR - allowing for stressing VaR and ES downwards

- amendment analogous to the

`stress()`

:- parameter
`x`

can have missing column names.

- parameter
`stress_moment()`

:- additional parameter
`show`

; if`TRUE`

(default is`FALSE`

), the result of`nleqslv()`

is printed.

- additional parameter